{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 项目简介：\n",
    "\n",
    "该数据集包含了学生的特征数据以及成绩数据，通过建立机器学习模型，来对学生的成绩进行预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入该任务需要用到的包\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns;sns.set()\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>NationalITy</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Relation</th>\n",
       "      <th>raisedhands</th>\n",
       "      <th>VisITedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
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       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender NationalITy PlaceofBirth     StageID GradeID SectionID Topic  \\\n",
       "0      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "1      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "2      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "3      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "4      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "\n",
       "  Semester Relation  raisedhands  VisITedResources  AnnouncementsView  \\\n",
       "0        F   Father           15                16                  2   \n",
       "1        F   Father           20                20                  3   \n",
       "2        F   Father           10                 7                  0   \n",
       "3        F   Father           30                25                  5   \n",
       "4        F   Father           40                50                 12   \n",
       "\n",
       "   Discussion ParentAnsweringSurvey ParentschoolSatisfaction  \\\n",
       "0          20                   Yes                     Good   \n",
       "1          25                   Yes                     Good   \n",
       "2          30                    No                      Bad   \n",
       "3          35                    No                      Bad   \n",
       "4          50                    No                      Bad   \n",
       "\n",
       "  StudentAbsenceDays Class  \n",
       "0            Under-7     M  \n",
       "1            Under-7     M  \n",
       "2            Above-7     L  \n",
       "3            Above-7     L  \n",
       "4            Above-7     M  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入数据集\n",
    "\n",
    "df = pd.read_csv('D:\\\\Py_dataset\\\\StudentPerformance.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 特征说明\n",
    "\n",
    "* gender：性别\n",
    "* Nationality：国籍\n",
    "* PlaceofBirth：出生地\n",
    "* StageID：学校级别（小学，中学，高中）\n",
    "* GradeID：年纪（G01-G12）\n",
    "* SectionID：班级\n",
    "* Topic：科目\n",
    "* Semester：学期（春季学期，秋季学期）\n",
    "* Relation：孩子家庭教育的负责人（父亲，母亲）\n",
    "* raisehands：课堂举手次数\n",
    "* VislTedResources：学生浏览在线课件的次数\n",
    "* AnnoucementsView：学生浏览学校公告的册数\n",
    "* Discussion：学生参与课堂讨论的次数\n",
    "* ParentAnsweringSurvey：家长是否填写了学校的问卷调查（是，否）\n",
    "* ParentschoolSatisfaction：家长对于学校的满意程度（好，不好）\n",
    "* StudentAbsenceDays：学生缺勤次数\n",
    "* Class：成绩等级"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 17)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                      0\n",
       "NationalITy                 0\n",
       "PlaceofBirth                0\n",
       "StageID                     0\n",
       "GradeID                     0\n",
       "SectionID                   0\n",
       "Topic                       0\n",
       "Semester                    0\n",
       "Relation                    0\n",
       "raisedhands                 0\n",
       "VisITedResources            0\n",
       "AnnouncementsView           0\n",
       "Discussion                  0\n",
       "ParentAnsweringSurvey       0\n",
       "ParentschoolSatisfaction    0\n",
       "StudentAbsenceDays          0\n",
       "Class                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看缺失值\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 480 entries, 0 to 479\n",
      "Data columns (total 17 columns):\n",
      "gender                      480 non-null object\n",
      "NationalITy                 480 non-null object\n",
      "PlaceofBirth                480 non-null object\n",
      "StageID                     480 non-null object\n",
      "GradeID                     480 non-null object\n",
      "SectionID                   480 non-null object\n",
      "Topic                       480 non-null object\n",
      "Semester                    480 non-null object\n",
      "Relation                    480 non-null object\n",
      "raisedhands                 480 non-null int64\n",
      "VisITedResources            480 non-null int64\n",
      "AnnouncementsView           480 non-null int64\n",
      "Discussion                  480 non-null int64\n",
      "ParentAnsweringSurvey       480 non-null object\n",
      "ParentschoolSatisfaction    480 non-null object\n",
      "StudentAbsenceDays          480 non-null object\n",
      "Class                       480 non-null object\n",
      "dtypes: int64(4), object(13)\n",
      "memory usage: 63.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>MiddleSchool</td>\n",
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       "      <th>freq</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>270</td>\n",
       "      <td>292</td>\n",
       "      <td>289</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "       gender NationalITy PlaceofBirth       StageID GradeID SectionID Topic  \\\n",
       "count     480         480          480           480     480       480   480   \n",
       "unique      2          14           14             3      10         3    12   \n",
       "top         M          KW       KuwaIT  MiddleSchool    G-02         A    IT   \n",
       "freq      305         179          180           248     147       283    95   \n",
       "mean      NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "std       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "min       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "25%       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "50%       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "75%       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "max       NaN         NaN          NaN           NaN     NaN       NaN   NaN   \n",
       "\n",
       "       Semester Relation  raisedhands  VisITedResources  AnnouncementsView  \\\n",
       "count       480      480   480.000000        480.000000         480.000000   \n",
       "unique        2        2          NaN               NaN                NaN   \n",
       "top           F   Father          NaN               NaN                NaN   \n",
       "freq        245      283          NaN               NaN                NaN   \n",
       "mean        NaN      NaN    46.775000         54.797917          37.918750   \n",
       "std         NaN      NaN    30.779223         33.080007          26.611244   \n",
       "min         NaN      NaN     0.000000          0.000000           0.000000   \n",
       "25%         NaN      NaN    15.750000         20.000000          14.000000   \n",
       "50%         NaN      NaN    50.000000         65.000000          33.000000   \n",
       "75%         NaN      NaN    75.000000         84.000000          58.000000   \n",
       "max         NaN      NaN   100.000000         99.000000          98.000000   \n",
       "\n",
       "        Discussion ParentAnsweringSurvey ParentschoolSatisfaction  \\\n",
       "count   480.000000                   480                      480   \n",
       "unique         NaN                     2                        2   \n",
       "top            NaN                   Yes                     Good   \n",
       "freq           NaN                   270                      292   \n",
       "mean     43.283333                   NaN                      NaN   \n",
       "std      27.637735                   NaN                      NaN   \n",
       "min       1.000000                   NaN                      NaN   \n",
       "25%      20.000000                   NaN                      NaN   \n",
       "50%      39.000000                   NaN                      NaN   \n",
       "75%      70.000000                   NaN                      NaN   \n",
       "max      99.000000                   NaN                      NaN   \n",
       "\n",
       "       StudentAbsenceDays Class  \n",
       "count                 480   480  \n",
       "unique                  2     3  \n",
       "top               Under-7     M  \n",
       "freq                  289   211  \n",
       "mean                  NaN   NaN  \n",
       "std                   NaN   NaN  \n",
       "min                   NaN   NaN  \n",
       "25%                   NaN   NaN  \n",
       "50%                   NaN   NaN  \n",
       "75%                   NaN   NaN  \n",
       "max                   NaN   NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relation: ['Father' 'Mum']\n"
     ]
    }
   ],
   "source": [
    "#查看类别特征\n",
    "\n",
    "print('Relation:',df['Relation'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stage: ['lowerlevel' 'MiddleSchool' 'HighSchool']\n"
     ]
    }
   ],
   "source": [
    "print('Stage:',df['StageID'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StudentAbsenceDays: ['Under-7' 'Above-7']\n"
     ]
    }
   ],
   "source": [
    "print('StudentAbsenceDays:',df['StudentAbsenceDays'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class: ['M' 'L' 'H']\n"
     ]
    }
   ],
   "source": [
    "print('Class:',df['Class'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930a4b3e80>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#检查数据集是否平衡\n",
    "\n",
    "sns.countplot('Class',order = ['L','M','H'],data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "三类成绩分布的数量没有较大偏差，所以该数据集是一个样本均衡的数据集。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.数据可视乎\n",
    "\n",
    "下面对类别数据进行可视化，对特征与结果分布有一个直观的认识。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1分类特征之间的可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930a7bab70>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'gender',order = ['M','F'],data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930a836630>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#了解学科的分布情况\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(14,8)})\n",
    "sns.countplot(x = 'Topic',data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930a8b4c50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#了解课程和成绩的关系\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(16,10)})\n",
    "sns.countplot(x = 'Topic',hue = 'Class',hue_order = ['L','M','H'],data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930a8b4a58>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAEKCAYAAADgu3feAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAcbElEQVR4nO3de3BU9eH38c8mm4RLkEvcBQoO86sgqVChjyhGMakglxBSSqCVi0as1DsiVhBChKGWohQFHX602lIcEWohRoI8ccGCpUBAICrUFhUVeATjZsMlJEBuu+f5Q7s2cjEJu3uSb96vGWdyzp79ns86s/vh3B2WZVkCAABGiLI7AAAACB2KHQAAg1DsAAAYhGIHAMAgFDsAAAah2AEAMEhYi728vFwjRozQkSNHas1/5ZVXdMcddwSnv/jiC02YMEHDhg3T/fffr9OnT4czFgAAxgpbse/du1fjxo3ToUOHas3/5JNP9OKLL9aaN3fuXI0fP14ej0e9e/fW0qVLwxULAACjOcJ1g5pZs2Zp1KhRmj59ul5++WV17dpVVVVVmjhxojIyMpSXl6cVK1aourpa/fv3165du+R0OlVUVKTbb79dmzZtqtf6Tpw4rUCAe+0AAMwXFeVQ+/atz/uaM1wrnTdv3jnznnnmGY0ePVpdu3YNzjtx4oTi4+PldH4VxeVyyev11nt9gYBFsQMAmr2wFfu3bd++XUVFRZo5c6beeeed4HzLsuRwOGot++3pukhIiL/kjAAANHURK/b169frwIEDGjlypM6cOaOSkhI98sgj+t3vfqeysjL5/X5FR0fL5/PJ7XbXe/xjx8rZYgcANAtRUY4LbtBGrNjnz58f/Pudd97RkiVLtHjxYklSv379lJ+fr/T0dK1du1bJycmRigUAMJhlWTpxwqeqqgpJTW3jz6HY2BZq395Vrz3ZESv2i5kzZ45mzJih3//+9+rcubOeffZZuyMBAAxQXl4qh8Ohjh27yuFoWrdusayATp4sUXl5qdq0aVfn94XtrPhIY1c8AODbiouPqEOHjnI6Y+yO0iA1NdU6ftwrt7trrfkX2xXftP75AgBAPQQCfkVHN4qd0w0SHe1UIOCv13sodgCA0RpypVVj0ZDsTfefMQi59m1j5YyNsztGxNRUVepEaZXdMQA0MiUlJVq8+Hc6cuRz1dRUKyVloNLSfqInnpihZctW2B3vO1HsCHLGxqlwwSS7Y0TMtdP/JIliB/CNmpoaPf74VN1334O67robVFVVpezsx7Vjx3a7o9UZxQ4AwNd2796prl276rrrbpAkxcbG6pFHHtPx48eUn/+GJOndd/foj39cqtOnT6tly1aaN2+B2rVrr7lzs3XkyP9TVFS0Zs6cre7de2jRogXau/d9BQJ+Pfzwr9Sv3/Vh/wwUOwAAXzt48DN9//vda8373ve61DrWnZu7Wr/97UK1b99BS5c+r40bPUpM/IFiYmK0fPkq7dmzS/v2va/WrVvr448/0ksvrdJnn32qzZvfikixc/IcAABBjuCzSy4kK2uOdu3aqRde+F+9806BKirO6n/+50rt2/e+Hn98qoqLvUpL+4lcLrdOny7XlCn36733CjV27O0R+QQUOwAAX+vRo4cOHPi41ryPPvpQS5c+L+mrO9k99NA9+vLLIl13XX/deutQWZal9u3b65VX1mjIkOHauvXvmjs3W06nU8uWvaKf/3y8Pvzw33r44Xsj8hkodgAAvvajH/XTwYOfaffunZKkiooKvfDCEvXt+yNJ0qlTpTp16pTuuOMu9e59jXbuLJBlWdq27R96/vlnNGjQYD366OP67LNPdODAR8rKekw33jhAjz02Qz6fT35//a5JbwiOsQMA8DWn06nf/vZ3euaZp7RkyXOqrq7SrbcO1Y033qz8/PVq27adkpIGaMKEMYqOdurqq3vJ6/1Sd955tzye/6vMzNsUExOrRx99XD169FTnzt/THXfcptjYGD3wwMOKjo4O+2fglrIIcrnaNLvL3Xy+MrtjAAijL788rE6dutkd45Kc7zNwS1kAAJoJih0AAINQ7AAAGIRiBwDAIBQ7AAAGodgBADAIxQ4AgEG4QQ0AoFlpc1kLtYiLCfm4FZXVKjtVcdFl3n13j/785xe1ZMmLIV//f1DsAIBmpUVcjMZPXxnycVctmKAyXbzYI4Fd8QAAGIRiBwDAIGEv9vLyco0YMUJHjhyRJP31r3/ViBEjlJ6erpkzZ6qqqkqStH//fmVkZGjo0KGaNWuWampqwh0NAADjhLXY9+7dq3HjxunQoUOSpIMHD2rZsmV69dVXtW7dOgUCAa1atUqSNG3aNM2ePVsbNmyQZVlavXp1OKMBAGCksBb76tWrNWfOHLndbklSbGys5syZo/j4eDkcDl111VX64osvdPToUVVUVKhv376SpIyMDHk8nnBGAwDASGE9K37evHm1prt06aIuXbpIko4fP66VK1dq/vz5Ki4ulsvlCi7ncrnk9Xrrta4LPb4OuBiXq43dEQCEUXFxlJzO2tuwlVU1WrVgQsjXVVlVc866vi06Okr79r2vwYNvDs4bNmy4Hn981gXfExUVVa/fKlsud/N6vZo0aZJGjx6t/v37q7CwUA6HI/i6ZVm1puuC57FfuuZYcjyPHTBbIBBQTU2g1rxTpWdtSiP16fN/9I9/7Dpn/rcz/rdAIHDOb1Wjeh77p59+qrFjx2rUqFF68MEHJUmdOnWSz+cLLlNSUhLcfQ8AAOouosVeXl6uu+++W1OmTNEvfvGL4PwuXbooLi5OhYWFkqS8vDwlJydHMhoAAEaI6K74nJwclZSUaPny5Vq+fLkkaeDAgZoyZYoWLlyo7OxslZeXq1evXsrMzIxkNAAAjOCwLMuIA9McY790LlcbFS6YZHeMiLl2+p84xg4Y7ssvD6tTp252x7gk5/sMjeoYOwAACB+KHQAAg/B0NwBAs9K+baycsXEhH7emqlInSqsuukxR0Rf62c9+op/8ZJSmT//m2vUDBz7SXXdNUFbWHA0fnn5JOSh2AECz4oyNC8v5RNdO/5Okixe7JLVt21bvvLNDfr9f0dHRkqRNm95Su3btQ5KDXfEAAERQy5atdNVVPbV373vBebt27VS/fteHZHyKHQCACLvllsF6++1NkqT9+/+l7t17KCYmJiRjU+wAAETYgAHJ2rmzQIFAQJs2vaWBAweHbGyKHQCACGvVqpW6d++hffve17vv7g7ZbniJYgcAwBYDB96qP/xhiXr2vFpOZ+jOZafYAQCwwU03JevAgY80aFDodsNL3FIW/4VbygIwzflux2rndewNUd9bynIdOwCgWfmqfENfwI0Fu+IBADAIxQ4AgEEodgAADEKxAwBgEIodAACDUOwAABiEy90AAM3KZW3jFBcbG/JxK6uqdKq08qLLFBV9ocmT71VOzhu15g8Y0E/btu0JSQ6KHQDQrMTFxmri8ikhH/elu56TdPFijwSK/Tu0uayFWsSF5lF6AACEG8X+HVrExWj89JV2x4iIVQsm2B0BAIxXUuLTxInjwzZ+2Iu9vLxcY8eO1R/+8Ad17dpVBQUFmj9/viorK5WamqqpU6dKkvbv369Zs2bp9OnT6tevn+bOnRvSp90AANAYXH65Sy+9tKrWvAED+oVs/LCeFb93716NGzdOhw4dkiRVVFQoKytLS5cuVX5+vj744ANt2bJFkjRt2jTNnj1bGzZskGVZWr16dTijAQBgpLAW++rVqzVnzhy53W5J0r59+9StWzddccUVcjqdSk9Pl8fj0dGjR1VRUaG+fftKkjIyMuTxeMIZDQAAI4V1X/e8efNqTRcXF8vlcgWn3W63vF7vOfNdLpe8Xm+91nWhx9cBF+NytbE7AoAwKi6OktNZexu2srrq6zPYQ6uyuuqcdX1bdPRXr59vuQu9Nyoqql6/VRE9iB0IBORwOILTlmXJ4XBccH59hOt57Pzwm43nsQNmCwQCqqkJ1Jp36mSl7Losze3upJycN87JtG3bnnPm/UcgEDjnt+piz2OP6J3nOnXqJJ/PF5z2+Xxyu93nzC8pKQnuvgcAAHUX0WLv06ePDh48qMOHD8vv92v9+vVKTk5Wly5dFBcXp8LCQklSXl6ekpOTIxkNAAAjRHRXfFxcnJ566ilNnjxZlZWVSklJ0bBhwyRJCxcuVHZ2tsrLy9WrVy9lZmZGMhoAwFANObzbWFhW/Q8xR6TYN2/eHPw7KSlJ69atO2eZxMRE5eTkRCIOAKCZiIqKlt9fI6ezad5B1O+vUVRUdL3ew9PdAADGatkyXmVlJ2VZ5z8xrTGzrIDKyk6oZcv6XfXFrd0AAMaKj2+rEyd88nqPSAr9lVPh5VBsbAvFx7et17sodgCAsRwOhzp0aF5XWbErHgAAg1DsAAAYhGIHAMAgFDsAAAah2AEAMAjFDgCAQSh2AAAMQrEDAGAQih0AAINQ7AAAGIRiBwDAIBQ7AAAGodgBADAIxQ4AgEEodgAADEKxAwBgEIodAACDUOwAABjElmLPy8tTWlqa0tLS9PTTT0uS9u/fr4yMDA0dOlSzZs1STU2NHdEAAGjSIl7sZ8+e1bx587RixQrl5eVpz549Kigo0LRp0zR79mxt2LBBlmVp9erVkY4GAECTF/Fi9/v9CgQCOnv2rGpqalRTUyOn06mKigr17dtXkpSRkSGPxxPpaAAANHnOSK8wPj5eU6ZMUWpqqlq2bKnrrrtOMTExcrlcwWVcLpe8Xm+kowEA0ORFvNg//PBDvfbaa3r77bfVpk0bPfbYY9q+fbscDkdwGcuyak3XRUJCfKijohlwudrYHQEAQqpOxe71etWxY8da8z755BN179693ivctm2bkpKSlJCQIOmr3e7Lli2Tz+cLLlNSUiK3212vcY8dK1cgYNU7z3fhh99sPl+Z3REAoN6iohwX3KC96DH2kydP6uTJk/rlL3+p0tLS4HRJSYkeeuihBoVJTExUQUGBzpw5I8uytHnzZl1//fWKi4tTYWGhpK/Omk9OTm7Q+AAANGcX3WL/1a9+pe3bt0uS+vfv/82bnE4NHTq0QSscMGCA/v3vfysjI0MxMTH64Q9/qHvuuUeDBw9Wdna2ysvL1atXL2VmZjZofAAAmrOLFvuyZcskSTNnztT8+fNDttJ77rlH99xzT615iYmJysnJCdk6AABojup0jH3+/Pk6evSoSktLZVnfHMfu1atX2IIBAID6q1OxP//881q2bFnwhDdJcjgc2rRpU9iCAUBz0b5trJyxcXbHiJiaqkqdKK2yO4ax6lTsa9eu1caNG885Mx4AcOmcsXEqXDDJ7hgR88NHf99srjiqrKrSqdLKiK6zTsXeuXNnSh0AEBKxzhhNXD7F7hgR8dJdz0lqhMWelJSkBQsWaNCgQWrRokVwPsfYAQBoXOpU7Lm5uZJU6/7tHGMHAKDxqVOxb968Odw5AABACNSp2JcvX37e+XfddVdIwwAAgEtTp2L/+OOPg39XVVVp9+7dSkpKClsoAADQMHW+Qc1/83q9mjVrVlgCAQCAhrvoQ2AupGPHjjp69GioswAAgEtU72PslmXpgw8+qHUXOgAA0DjU+xi79NUNa6ZPnx6WQAAAoOHqdYz96NGjqqmpUbdu3cIaCgAANEydiv3w4cN64IEHVFxcrEAgoPbt2+uFF17QlVdeGe58AACgHupU7L/+9a81adIkjRo1SpL02muvae7cuXr55ZfDGg4Ip6qaah5EAcA4dSr2Y8eOBUtdkkaPHq2XXnopXJmAiOBBFABMVKfL3fx+v06ePBmcPn78eNgCAQCAhqvTFvvtt9+u2267TampqXI4HMrPz9edd94Z7mwAAKCe6rTFnpKSIkmqrq7Wp59+Kq/Xq8GDB4c1GAAAqL86bbHPmDFDEyZMUGZmpiorK/WXv/xFWVlZ+uMf/xjufAAAoB7qtMV+4sQJZWZmSpLi4uI0ceJE+Xy+sAYDAAD1V+eT57xeb3C6pKRElmU1eKWbN29WRkaGUlNT9Zvf/EaSVFBQoPT0dA0ZMkSLFi1q8NgAADRnddoVP3HiRP30pz/VzTffLIfDoYKCggbfUvbzzz/XnDlztGbNGiUkJOjOO+/Uli1bNGfOHK1YsUKdO3fWvffeqy1btgSP7QMAgLqpU7GPGTNGvXv31s6dOxUdHa27775bV111VYNW+NZbb2n48OHq1KmTJGnRokU6fPiwunXrpiuuuEKSlJ6eLo/HQ7EDAFBPdSp2SUpMTFRiYuIlr/Dw4cOKiYnRfffdp6KiIv34xz9Wjx495HK5gsu43e5au/7rIiEh/pKzASZrLnfZAxqbSH/36lzsoeL3+7Vnzx6tWLFCrVq10v33368WLVrI4XAEl7Esq9Z0XRw7Vq5AoOHH/S+EH0OYwucrszsCLoDfGbOF47sXFeW44AZtxIv98ssvV1JSkjp06CBJuvXWW+XxeBQdHR1cxufzye12RzoaAABNXp3Oig+lW265Rdu2bdOpU6fk9/u1detWDRs2TAcPHtThw4fl9/u1fv16JScnRzoaAABNXsS32Pv06aNJkyZp/Pjxqq6u1k033aRx48bp+9//viZPnqzKykqlpKRo2LBhkY4GAECTF/Fil746y37MmDG15iUlJWndunV2xAEAwBgR3xUPAADCh2IHAMAgFDsAAAah2AEAMAjFDgCAQSh2AAAMQrEDAGAQih0AAINQ7AAAGIRiBwDAIBQ7AAAGodgBADAIxQ4AgEEodgAADEKxAwBgEIodAACDUOwAABiEYgcAwCAUOwAABqHYAQAwCMUOAIBBKHYAAAxia7E//fTTmjFjhiRp//79ysjI0NChQzVr1izV1NTYGQ0AgCbJtmLfsWOHXn/99eD0tGnTNHv2bG3YsEGWZWn16tV2RQMAoMmypdhPnjypRYsW6b777pMkHT16VBUVFerbt68kKSMjQx6Px45oAAA0aU47Vjp79mxNnTpVRUVFkqTi4mK5XK7g6y6XS16vt15jJiTEhzQjYBqXq43dEYBmKdLfvYgX+5o1a9S5c2clJSUpNzdXkhQIBORwOILLWJZVa7oujh0rVyBghTSrxI8hzOHzldkdARfA74zZwvHdi4pyXHCDNuLFnp+fL5/Pp5EjR6q0tFRnzpyRw+GQz+cLLlNSUiK32x3paAAANHkRL/bly5cH/87NzdWuXbs0f/58jRgxQoWFhbr22muVl5en5OTkSEcDAKDJs+UY+/ksXLhQ2dnZKi8vV69evZSZmWl3JAAAmhxbiz0jI0MZGRmSpMTEROXk5NgZBwCAJo87zwEAYBCKHQAAg1DsAAAYhGIHAMAgFDsAAAah2AEAMAjFDgCAQSh2AAAMQrEDAGAQih0AAINQ7AAAGIRiBwDAIBQ7AAAGodgBADAIxQ4AgEEodgAADEKxAwBgEIodAACDOO0OAADn0+ayFmoRF2N3DKDJodgBNEot4mI0fvpKu2NExKoFE+yOAIOwKx4AAIPYUuxLlixRWlqa0tLStGDBAklSQUGB0tPTNWTIEC1atMiOWAAANHkRL/aCggJt27ZNr7/+utauXat//etfWr9+vbKysrR06VLl5+frgw8+0JYtWyIdDQCAJi/ixe5yuTRjxgzFxsYqJiZGV155pQ4dOqRu3brpiiuukNPpVHp6ujweT6SjAQDQ5EW82Hv06KG+fftKkg4dOqQ333xTDodDLpcruIzb7ZbX6410NAAAmjzbzoo/cOCA7r33Xk2fPl3R0dE6dOhQ8DXLsuRwOOo1XkJCfIgTAmZxudrYHQFoliL93bOl2AsLC/Xwww8rKytLaWlp2rVrl3w+X/B1n88nt9tdrzGPHStXIGCFOio/hjCGz1dmd4R64bsHU4TjuxcV5bjgBm3Ed8UXFRXpwQcf1MKFC5WWliZJ6tOnjw4ePKjDhw/L7/dr/fr1Sk5OjnQ0AACavIhvsS9btkyVlZV66qmngvPGjh2rp556SpMnT1ZlZaVSUlI0bNiwSEcDAKDJi3ixZ2dnKzs7+7yvrVu3LsJpAAAwC3eeAwDAIBQ7AAAGodgBADAIxQ4AgEEodgAADEKxAwBgEIodAACDUOwAABiEYgcAwCAUOwAABqHYAQAwCMUOAIBBKHYAAAxCsQMAYBCKHQAAg1DsAAAYhGIHAMAgFDsAAAah2AEAMAjFDgCAQSh2AAAMQrEDAGCQRlXsb7zxhoYPH64hQ4Zo5cqVdscBAKDJcdod4D+8Xq8WLVqk3NxcxcbGauzYserfv7+6d+9udzQAAJqMRlPsBQUFuuGGG9SuXTtJ0tChQ+XxePTQQw/V6f1RUY6wZbu8feuwjd3YxF6WYHeEiLo8voPdESImnN+RcOG7Zy6+e+Eb02FZlhXyNTbACy+8oDNnzmjq1KmSpDVr1mjfvn168sknbU4GAEDT0WiOsQcCATkc3/wLxLKsWtMAAOC7NZpi79Spk3w+X3Da5/PJ7XbbmAgAgKan0RT7jTfeqB07duj48eM6e/asNm7cqOTkZLtjAQDQpDSak+c6duyoqVOnKjMzU9XV1RozZoyuueYau2MBANCkNJqT5wAAwKVrNLviAQDApaPYAQAwCMUOAIBBKHYAAAxCsSPijhw5op49e2r27Nm15u/fv189e/ZUbm6uTcmA5uHIkSPq3bu3Ro4cWeu/oqIiu6MhBBrN5W5oXtq1a6etW7fK7/crOjpakpSfn68OHZrP/aMBO7ndbuXl5dkdA2HAFjts0bp1a/3gBz/Q7t27g/O2b9+uG2+80cZUAND0scUO26SmpmrDhg264YYbtG/fPvXs2VPcVgGIjOLiYo0cOTI4nZ6erkmTJtmYCKFCscM2AwcO1OLFixUIBPTmm28qNTVV+fn5dscCmgV2xZuLXfGwTevWrZWYmKjCwkLt3LmT3fAAEAIUO2yVmpqqZ555Rr1795bTyQ4kALhUFDtsdcstt2j//v0aPny43VEAwAg8BAYAAIOwxQ4AgEEodgAADEKxAwBgEIodAACDUOwAABiEYgcQUh6PR3fccYfdMYBmi2IHAMAg3OoLaKZefPFF5eTkqHXr1urXr582bdokj8ejhQsXavfu3fL7/br66quVnZ2t+Ph4DRw4UKNGjdKOHTtUVFSkkSNH6pFHHpEkPffcc3rjjTfUrl07devWLbiOqqqqi453zTXX6KOPPtKjjz6qwYMH2/W/AjAKW+xAM7R161bl5uYqJydHubm5On36tKSvyj46Olq5ublat26d3G63Fi5cGHzfmTNntGrVKr366qv685//rM8//1x/+9vftHHjRq1du1avvvqqysvLg8t/13g9evTQm2++SakDIcQWO9AMbdmyRcOGDdNll10mSZowYYJ27typv//97yorK1NBQYEkqbq6WgkJCcH3DRo0SJLUsWNHJSQkqLS0VDt27NDgwYMVHx8vSRo9erRWrFghSd85Xr9+/cL/YYFmhmIHmiGn06n/vpt0dHS0JCkQCCgrK0spKSmSpNOnT6uysjK4XFxcXPBvh8MRHON8Y9VlvFatWoXyYwEQu+KBZiklJUUbN25UWVmZJCknJ0eSNGDAAK1cuVJVVVUKBAJ64okn9Oyzz150rOTkZHk8Hp06dUqBQKDWM74bMh6AS0OxA81QUlKSfv7zn+u2225TRkaGysrK1LJlSz3wwAPq0qWLRo0apeHDh8uyLM2YMeOiY6WkpGj06NEaPXq0fvazn6lNmzbB1xoyHoBLw9PdgGbon//8p9577z1lZmZKkpYvX669e/dq8eLFNicDcKkodqAZKi8vV1ZWlj777DM5HA517txZTz75pDp27Gh3NACXiGIHAMAgHGMHAMAgFDsAAAah2AEAMAjFDgCAQSh2AAAMQrEDAGCQ/w9KO5ztylbWCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#了解性别和成绩的关系\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(8,4)})\n",
    "sns.countplot(x = 'gender',hue = 'Class',hue_order = ['L','M','H'],data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930aa0b7f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#了解性别和学科的相关性\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(10,6)})\n",
    "sns.countplot(x = 'Topic',hue = 'gender',hue_order = ['M','F'],data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic</th>\n",
       "      <th>gender</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>M</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Biology</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Biology</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chemistry</td>\n",
       "      <td>F</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Topic gender  Count\n",
       "0     Arabic      F     16\n",
       "1     Arabic      M     43\n",
       "2    Biology      F     10\n",
       "3    Biology      M     20\n",
       "4  Chemistry      F     12"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp = df[['Topic','gender']]\n",
    "df_temp['Count'] = 1\n",
    "df_temp = df_temp.groupby(['Topic','gender']).agg('sum').reset_index()\n",
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Biology</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chemistry</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>English</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>French</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Topic  Count\n",
       "0     Arabic     59\n",
       "1    Biology     30\n",
       "2  Chemistry     24\n",
       "3    English     45\n",
       "4     French     65"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp2 = df_temp.groupby('Topic').agg('sum').reset_index()\n",
    "df_temp2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic</th>\n",
       "      <th>gender</th>\n",
       "      <th>Count_x</th>\n",
       "      <th>Count_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>M</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Biology</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Biology</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chemistry</td>\n",
       "      <td>F</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Topic gender  Count_x  Count_y\n",
       "0     Arabic      F       16       59\n",
       "1     Arabic      M       43       59\n",
       "2    Biology      F       10       30\n",
       "3    Biology      M       20       30\n",
       "4  Chemistry      F       12       24"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp = pd.merge(df_temp,df_temp2,on = 'Topic',how = 'left')\n",
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic</th>\n",
       "      <th>gender</th>\n",
       "      <th>Count_x</th>\n",
       "      <th>Count_y</th>\n",
       "      <th>gender proportion in topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>59</td>\n",
       "      <td>0.271186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arabic</td>\n",
       "      <td>M</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "      <td>0.728814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Biology</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Biology</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chemistry</td>\n",
       "      <td>F</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Topic gender  Count_x  Count_y  gender proportion in topic\n",
       "0     Arabic      F       16       59                    0.271186\n",
       "1     Arabic      M       43       59                    0.728814\n",
       "2    Biology      F       10       30                    0.333333\n",
       "3    Biology      M       20       30                    0.666667\n",
       "4  Chemistry      F       12       24                    0.500000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp['gender proportion in topic'] = df_temp['Count_x']/df_temp['Count_y']\n",
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930aec7f98>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#了解班级和成绩的相关性\n",
    "\n",
    "sns.countplot(x = 'SectionID',hue = 'Class',hue_order = ['L','M','H'],data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 480 entries, 0 to 479\n",
      "Data columns (total 17 columns):\n",
      "gender                      480 non-null object\n",
      "NationalITy                 480 non-null object\n",
      "PlaceofBirth                480 non-null object\n",
      "StageID                     480 non-null object\n",
      "GradeID                     480 non-null object\n",
      "SectionID                   480 non-null object\n",
      "Topic                       480 non-null object\n",
      "Semester                    480 non-null object\n",
      "Relation                    480 non-null object\n",
      "raisedhands                 480 non-null int64\n",
      "VisITedResources            480 non-null int64\n",
      "AnnouncementsView           480 non-null int64\n",
      "Discussion                  480 non-null int64\n",
      "ParentAnsweringSurvey       480 non-null object\n",
      "ParentschoolSatisfaction    480 non-null object\n",
      "StudentAbsenceDays          480 non-null object\n",
      "Class                       480 non-null object\n",
      "dtypes: int64(4), object(13)\n",
      "memory usage: 63.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2分类型特征和数字型特征之间的可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930b232860>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(2,2,figsize = (14,10))\n",
    "\n",
    "sns.barplot(x = 'Class',y = 'VisITedResources',data = df,order = ['L','M','H'],ax = axes[0,0])\n",
    "sns.barplot(x = 'Class',y = 'AnnouncementsView',data = df,order = ['L','M','H'],ax = axes[0,1])\n",
    "sns.barplot(x = 'Class',y = 'raisedhands',data = df,order = ['L','M','H'],ax = axes[1,0])\n",
    "sns.barplot(x = 'Class',y = 'Discussion',data = df,order = ['L','M','H'],ax = axes[1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，查看课件、学校公告、课堂发言、参与讨论的数量都对最终的成绩有积极影响。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930aa107b8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#了解不同性别，举手次数对学习成绩的影响\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(8,6)})\n",
    "sns.swarmplot(x = 'Class',y = 'raisedhands',hue = 'gender',order = ['L','M','H'],data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930b2ed1d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#了解上课参与讨论的积极程度与成绩的关系\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(8,6)})\n",
    "sns.boxplot(x = 'Class',y = 'Discussion',order = ['L','M','H'],data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出，参与课堂讨论的次数越多，其相应的成绩也越高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3数字型特征之间的可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930b3b1390>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(2,1,figsize = (8,8))\n",
    "\n",
    "sns.regplot(x = 'raisedhands',y = 'AnnouncementsView',order = 4,data = df,ax = axes[0])\n",
    "sns.regplot(x = 'raisedhands',y = 'Discussion',order = 4,data = df,ax = axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raisedhands</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>VisITedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>raisedhands</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.339386</td>\n",
       "      <td>0.691572</td>\n",
       "      <td>0.643918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discussion</th>\n",
       "      <td>0.339386</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.243292</td>\n",
       "      <td>0.417290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VisITedResources</th>\n",
       "      <td>0.691572</td>\n",
       "      <td>0.243292</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.594500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <td>0.643918</td>\n",
       "      <td>0.417290</td>\n",
       "      <td>0.594500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   raisedhands  Discussion  VisITedResources  \\\n",
       "raisedhands           1.000000    0.339386          0.691572   \n",
       "Discussion            0.339386    1.000000          0.243292   \n",
       "VisITedResources      0.691572    0.243292          1.000000   \n",
       "AnnouncementsView     0.643918    0.417290          0.594500   \n",
       "\n",
       "                   AnnouncementsView  \n",
       "raisedhands                 0.643918  \n",
       "Discussion                  0.417290  \n",
       "VisITedResources            0.594500  \n",
       "AnnouncementsView           1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#相关性矩阵（correlation matrix）\n",
    "\n",
    "corr = df[['raisedhands','Discussion','VisITedResources','AnnouncementsView']].corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930b83cb70>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#相关性矩阵的可视化\n",
    "\n",
    "sns.heatmap(corr,fmt = 'f',annot = True,xticklabels = corr.columns,yticklabels = corr.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1原始数据建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>NationalITy</th>\n",
       "      <th>PlaceofBirth</th>\n",
       "      <th>StageID</th>\n",
       "      <th>GradeID</th>\n",
       "      <th>SectionID</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Semester</th>\n",
       "      <th>Relation</th>\n",
       "      <th>raisedhands</th>\n",
       "      <th>VisITedResources</th>\n",
       "      <th>AnnouncementsView</th>\n",
       "      <th>Discussion</th>\n",
       "      <th>ParentAnsweringSurvey</th>\n",
       "      <th>ParentschoolSatisfaction</th>\n",
       "      <th>StudentAbsenceDays</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>Under-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>KW</td>\n",
       "      <td>KuwaIT</td>\n",
       "      <td>lowerlevel</td>\n",
       "      <td>G-04</td>\n",
       "      <td>A</td>\n",
       "      <td>IT</td>\n",
       "      <td>F</td>\n",
       "      <td>Father</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>No</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Above-7</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender NationalITy PlaceofBirth     StageID GradeID SectionID Topic  \\\n",
       "0      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "1      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "2      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "3      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "4      M          KW       KuwaIT  lowerlevel    G-04         A    IT   \n",
       "\n",
       "  Semester Relation  raisedhands  VisITedResources  AnnouncementsView  \\\n",
       "0        F   Father           15                16                  2   \n",
       "1        F   Father           20                20                  3   \n",
       "2        F   Father           10                 7                  0   \n",
       "3        F   Father           30                25                  5   \n",
       "4        F   Father           40                50                 12   \n",
       "\n",
       "   Discussion ParentAnsweringSurvey ParentschoolSatisfaction  \\\n",
       "0          20                   Yes                     Good   \n",
       "1          25                   Yes                     Good   \n",
       "2          30                    No                      Bad   \n",
       "3          35                    No                      Bad   \n",
       "4          50                    No                      Bad   \n",
       "\n",
       "  StudentAbsenceDays Class  \n",
       "0            Under-7     M  \n",
       "1            Under-7     M  \n",
       "2            Above-7     L  \n",
       "3            Above-7     L  \n",
       "4            Above-7     M  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练样本的大小: (336, 72)\n",
      "训练标签的大小: (336,)\n",
      "测试样本的大小: (144, 72)\n",
      "测试标签的大小: (144,)\n"
     ]
    }
   ],
   "source": [
    "labels = df['Class']\n",
    "features = df.drop('Class',axis = 1)\n",
    "features = pd.get_dummies(features)\n",
    "\n",
    "train_features,test_features,train_labels,test_labels = train_test_split(features,labels,test_size = 0.3,random_state = 0)\n",
    "\n",
    "print('训练样本的大小:',train_features.shape)\n",
    "print('训练标签的大小:',train_labels.shape)\n",
    "print('测试样本的大小:',test_features.shape)\n",
    "print('测试标签的大小:',test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6944444444444444\n"
     ]
    }
   ],
   "source": [
    "#导入逻辑回归模型\n",
    "\n",
    "LR = LogisticRegression()\n",
    "LR.fit(train_features,train_labels)\n",
    "prediction = LR.predict(test_features)\n",
    "\n",
    "score = accuracy_score(prediction,test_labels)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2删除部分特征建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练样本的大小: (336, 56)\n",
      "训练标签的大小: (336,)\n",
      "测试样本的大小: (144, 56)\n",
      "测试标签的大小: (144,)\n"
     ]
    }
   ],
   "source": [
    "df2 = df\n",
    "\n",
    "labels2 = df2['Class']\n",
    "features2 = df2.drop('Class',axis = 1)\n",
    "\n",
    "#删除部分特征\n",
    "features2 = features2.drop(['StageID','GradeID','SectionID'],axis = 1)\n",
    "\n",
    "features2 = pd.get_dummies(features2)\n",
    "\n",
    "#切分训练集个测试集\n",
    "train_features2,test_features2,train_labels2,test_labels2 = train_test_split(features2,labels2,test_size = 0.3,random_state = 0)\n",
    "\n",
    "print('训练样本的大小:',train_features2.shape)\n",
    "print('训练标签的大小:',train_labels2.shape)\n",
    "print('测试样本的大小:',test_features2.shape)\n",
    "print('测试标签的大小:',test_labels2.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6805555555555556\n"
     ]
    }
   ],
   "source": [
    "LR2 = LogisticRegression()\n",
    "LR2.fit(train_features2,train_labels2)\n",
    "prediction2 = LR2.predict(test_features2)\n",
    "\n",
    "score2 = accuracy_score(prediction2,test_labels2)\n",
    "print(score2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3增加新特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练样本的大小: (336, 57)\n",
      "训练标签的大小: (336,)\n",
      "测试样本的大小: (144, 57)\n",
      "测试标签的大小: (144,)\n"
     ]
    }
   ],
   "source": [
    "df3 = df\n",
    "labels3 = df3['Class']\n",
    "df3['DiscussionPlusVisit'] = df3['Discussion'] + df3['VisITedResources']\n",
    "features3 = df3.drop('Class',axis = 1)\n",
    "features3 = features3.drop(['StageID','GradeID','SectionID'],axis = 1)\n",
    "\n",
    "features3 = pd.get_dummies(features3)\n",
    "\n",
    "#切分训练集个测试集\n",
    "train_features3,test_features3,train_labels3,test_labels3 = train_test_split(features3,labels3,test_size = 0.3,random_state = 0)\n",
    "\n",
    "print('训练样本的大小:',train_features3.shape)\n",
    "print('训练标签的大小:',train_labels3.shape)\n",
    "print('测试样本的大小:',test_features3.shape)\n",
    "print('测试标签的大小:',test_labels3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6805555555555556\n"
     ]
    }
   ],
   "source": [
    "LR3 = LogisticRegression()\n",
    "LR3.fit(train_features3,train_labels3)\n",
    "prediction3 = LR3.predict(test_features3)\n",
    "\n",
    "score3 = accuracy_score(prediction3,test_labels3)\n",
    "print(score3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.集成算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8055555555555556\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "RF = RandomForestClassifier(n_estimators = 100)\n",
    "RF.fit(train_features,train_labels)\n",
    "RF_prediction = RF.predict(test_features)\n",
    "\n",
    "RF_score = accuracy_score(RF_prediction,test_labels)\n",
    "print(RF_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VisITedResources</td>\n",
       "      <td>0.125528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>raisedhands</td>\n",
       "      <td>0.121202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AnnouncementsView</td>\n",
       "      <td>0.102578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Discussion</td>\n",
       "      <td>0.072496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>StudentAbsenceDays_Above-7</td>\n",
       "      <td>0.071229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>StudentAbsenceDays_Under-7</td>\n",
       "      <td>0.061473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Relation_Father</td>\n",
       "      <td>0.030665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>ParentAnsweringSurvey_No</td>\n",
       "      <td>0.026344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>ParentAnsweringSurvey_Yes</td>\n",
       "      <td>0.026094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Relation_Mum</td>\n",
       "      <td>0.025920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>ParentschoolSatisfaction_Bad</td>\n",
       "      <td>0.017627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gender_F</td>\n",
       "      <td>0.017406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gender_M</td>\n",
       "      <td>0.017264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>ParentschoolSatisfaction_Good</td>\n",
       "      <td>0.015843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PlaceofBirth_KuwaIT</td>\n",
       "      <td>0.014050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>SectionID_B</td>\n",
       "      <td>0.011360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>SectionID_A</td>\n",
       "      <td>0.010841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>StageID_lowerlevel</td>\n",
       "      <td>0.010518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>GradeID_G-02</td>\n",
       "      <td>0.010122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NationalITy_KW</td>\n",
       "      <td>0.010091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PlaceofBirth_Jordan</td>\n",
       "      <td>0.009939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Semester_S</td>\n",
       "      <td>0.009596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Topic_IT</td>\n",
       "      <td>0.009519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NationalITy_Jordan</td>\n",
       "      <td>0.009326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>StageID_MiddleSchool</td>\n",
       "      <td>0.008973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>GradeID_G-08</td>\n",
       "      <td>0.008256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Semester_F</td>\n",
       "      <td>0.008214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Topic_Chemistry</td>\n",
       "      <td>0.007214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>GradeID_G-07</td>\n",
       "      <td>0.007025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Topic_Arabic</td>\n",
       "      <td>0.006731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>GradeID_G-06</td>\n",
       "      <td>0.004437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PlaceofBirth_Iraq</td>\n",
       "      <td>0.003973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>StageID_HighSchool</td>\n",
       "      <td>0.003479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Topic_Biology</td>\n",
       "      <td>0.003307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Topic_Spanish</td>\n",
       "      <td>0.003216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NationalITy_lebanon</td>\n",
       "      <td>0.003038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PlaceofBirth_USA</td>\n",
       "      <td>0.002885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Topic_Quran</td>\n",
       "      <td>0.002782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PlaceofBirth_Palestine</td>\n",
       "      <td>0.002774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Topic_Math</td>\n",
       "      <td>0.001991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NationalITy_Lybia</td>\n",
       "      <td>0.001918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NationalITy_Tunis</td>\n",
       "      <td>0.001850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>GradeID_G-11</td>\n",
       "      <td>0.001685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PlaceofBirth_Syria</td>\n",
       "      <td>0.001667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NationalITy_USA</td>\n",
       "      <td>0.001537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NationalITy_Iran</td>\n",
       "      <td>0.001490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NationalITy_Syria</td>\n",
       "      <td>0.001353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PlaceofBirth_Iran</td>\n",
       "      <td>0.001319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>GradeID_G-12</td>\n",
       "      <td>0.001317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PlaceofBirth_Tunis</td>\n",
       "      <td>0.001280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NationalITy_Egypt</td>\n",
       "      <td>0.001253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PlaceofBirth_Lybia</td>\n",
       "      <td>0.001065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PlaceofBirth_Morocco</td>\n",
       "      <td>0.000994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PlaceofBirth_Egypt</td>\n",
       "      <td>0.000923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>GradeID_G-09</td>\n",
       "      <td>0.000679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NationalITy_Morocco</td>\n",
       "      <td>0.000589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>GradeID_G-10</td>\n",
       "      <td>0.000467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NationalITy_venzuela</td>\n",
       "      <td>0.000237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>GradeID_G-05</td>\n",
       "      <td>0.000191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PlaceofBirth_venzuela</td>\n",
       "      <td>0.000096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          feature  importance\n",
       "1                VisITedResources    0.125528\n",
       "0                     raisedhands    0.121202\n",
       "2               AnnouncementsView    0.102578\n",
       "3                      Discussion    0.072496\n",
       "70     StudentAbsenceDays_Above-7    0.071229\n",
       "71     StudentAbsenceDays_Under-7    0.061473\n",
       "64                Relation_Father    0.030665\n",
       "66       ParentAnsweringSurvey_No    0.026344\n",
       "67      ParentAnsweringSurvey_Yes    0.026094\n",
       "65                   Relation_Mum    0.025920\n",
       "68   ParentschoolSatisfaction_Bad    0.017627\n",
       "4                        gender_F    0.017406\n",
       "5                        gender_M    0.017264\n",
       "69  ParentschoolSatisfaction_Good    0.015843\n",
       "24            PlaceofBirth_KuwaIT    0.014050\n",
       "48                    SectionID_B    0.011360\n",
       "47                    SectionID_A    0.010841\n",
       "36             StageID_lowerlevel    0.010518\n",
       "37                   GradeID_G-02    0.010122\n",
       "10                 NationalITy_KW    0.010091\n",
       "23            PlaceofBirth_Jordan    0.009939\n",
       "63                     Semester_S    0.009596\n",
       "57                       Topic_IT    0.009519\n",
       "9              NationalITy_Jordan    0.009326\n",
       "35           StageID_MiddleSchool    0.008973\n",
       "42                   GradeID_G-08    0.008256\n",
       "62                     Semester_F    0.008214\n",
       "52                Topic_Chemistry    0.007214\n",
       "41                   GradeID_G-07    0.007025\n",
       "50                   Topic_Arabic    0.006731\n",
       "..                            ...         ...\n",
       "40                   GradeID_G-06    0.004437\n",
       "22              PlaceofBirth_Iraq    0.003973\n",
       "34             StageID_HighSchool    0.003479\n",
       "51                  Topic_Biology    0.003307\n",
       "61                  Topic_Spanish    0.003216\n",
       "18            NationalITy_lebanon    0.003038\n",
       "31               PlaceofBirth_USA    0.002885\n",
       "59                    Topic_Quran    0.002782\n",
       "27         PlaceofBirth_Palestine    0.002774\n",
       "58                     Topic_Math    0.001991\n",
       "11              NationalITy_Lybia    0.001918\n",
       "16              NationalITy_Tunis    0.001850\n",
       "45                   GradeID_G-11    0.001685\n",
       "29             PlaceofBirth_Syria    0.001667\n",
       "17                NationalITy_USA    0.001537\n",
       "7                NationalITy_Iran    0.001490\n",
       "15              NationalITy_Syria    0.001353\n",
       "21              PlaceofBirth_Iran    0.001319\n",
       "46                   GradeID_G-12    0.001317\n",
       "30             PlaceofBirth_Tunis    0.001280\n",
       "6               NationalITy_Egypt    0.001253\n",
       "25             PlaceofBirth_Lybia    0.001065\n",
       "26           PlaceofBirth_Morocco    0.000994\n",
       "20             PlaceofBirth_Egypt    0.000923\n",
       "43                   GradeID_G-09    0.000679\n",
       "12            NationalITy_Morocco    0.000589\n",
       "44                   GradeID_G-10    0.000467\n",
       "19           NationalITy_venzuela    0.000237\n",
       "39                   GradeID_G-05    0.000191\n",
       "33          PlaceofBirth_venzuela    0.000096\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance = pd.DataFrame({'feature':features.columns,'importance':RF.feature_importances_})\n",
    "feature_importance = feature_importance.sort_values(by = 'importance',ascending = False)\n",
    "feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1930dc852b0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {'figure.figsize':(20,15)})\n",
    "\n",
    "sns.barplot(y = 'feature',x = 'importance',data = feature_importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
